Complexity and Strategy
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Complexity and Strategy EIASM Academic Council Prof. Joan E. Ricart IESE Business School October 11, 2006. Limits on classical organizational dynamics Complex adaptive systems Complexity in management literature An example: Corporate level decision in turbulent environments. Agenda.

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Complexity and strategy eiasm academic council

Complexity and Strategy

EIASM Academic Council

Prof. Joan E. RicartIESE Business School October 11, 2006


Agenda

Limits on classical organizational dynamics

Complex adaptive systems

Complexity in management literature

An example: Corporate level decision in turbulent environments.

Agenda


Complexity and strategy eiasm academic council

Classical Organizational Dynamics

The search for equilibrium

Analysis and

diagnostic

Of the current status

Choose corrective

action

Select and

Realization of

the future

Implement corrective

action

Feedback

Deviation from

plan

Results

Forecast vs. realized

BASED ON: The search of a goal and the need to adapt to the environment: Strategic Planning and Control Systems


Complexity and strategy eiasm academic council

Classical Organizational Dynamics

Limitations

  • 1. Not possible to use scenarios for all possible events.

  • 2. From the 70’s more difficult to forecast due to:

    • Deregulation and privatization

    • Globalization

    • Technological development

  • 3. Three key ignored factors:

    • The existence of positive feedbacks

    • Ambiguity and paradox are inherent to the firm

    • The social construction of reality


Four alternative views

Four Alternative Views

Subjective

Heuristics-based

Objective

Rule-based

Process Engineering

Systems

Dynamics

Established Order

Prospective Coherence

 Taylor, Demming, Hammer,

Argyris, Senge, Checkland,

Mathematical Complexity

Social Complexity

Emergent Order Retrospective Coherence

Stacey, Cilliers, Juarerro

Langton, Kauffman, Wolfram

Complexity sciences as an explanation of how novelty emerges


Complexity and strategy eiasm academic council

Complex Adaptive Systems: Definition

  • Nº of agents behaving according to their own principles of local interaction (“Microinteractions”)

  • No agent capable of determining patterns of whole system

  • No agent is “designer” from outside the system

  • CAS also display a broad category of dynamics

  • Stable equilibrium

  • Random chaos

  • Edge of chaos

  • Patterns of evolution emerge in the interaction between agents, neither by choice of “designer” nor by chance

Eg: food distribution in a big city


Complexity and strategy eiasm academic council

Complex Adaptive Systems: Modeling in biology

• Kauffman’s NK boolean networks (biology, genetics)

N entities or agents form the network (gene)

K= nº of connections

The different agents can take two values (0;1)

Each value has an associated “fitness value

• Network evolve trying to survive increasing their fitness

Fitness reflected by height of positions in a “fitness landscape”

K is high

“rugged landscape”

high number of attractors

extreme: properties of mathematical chaos

as conflicting constraints multiply

K=0

“smooth landscape”

stable attractor

survival strategy is easy to copy

remove “competitive advantage”

Highest fitness at intermediate levels of K “edge of chaos”


Complexity and strategy eiasm academic council

Complexity in management literature

1.Complexity sciences used as source of loose metaphors

  • Self-organized interaction driven by simple rules “hidden order”

  • Dynamics at “the edge of chaos”

  • Fitness landscapes as set of possible structures to choose

2.Complexity sciences as a framework about learning systems

  • NK modeling

  • Industry-level studies


Complexity and strategy eiasm academic council

“Self organizing based in Simple rules”

  • Idea: Managers should manage the context and allow self- organizing to arise fruitfully (Morgan, 1997; Eisenhardt & Brown, 1998)

  • Issue set of “simple rules” (Eisenhardt & Sull, 2001)

  • Let the organization evolve freely within them

  • Managers condition emergence

  • “Designed emergence” (Pascale, 1999)

  • They choose broadly what emerges

  • Implications

  • The message of complex sciences on how novelty emerges is lost

  • Designer of “simple rules” outside the system

  • No novelty, just unfolding of states within the simple rules

  • Message already present in Systems Dynamics

  • Emergence “allowed” only at superficial level

  • Control is centralized in “designer", not property of micro-interactions


Complexity and strategy eiasm academic council

NK networks in Social Science

  • Several papers use NK networks in social science

  • Levinthal (1997), Mc Kelvey (1999)

  • Problem: biology assumes total decomposability of the network

  • Firms are near decomposable systems (Simon, 1968)

  • Interactions within units more intense than between units

  • Solution: works assuming near decomposability (Gavetti, 1999; Gavetti, Levinthal & Rivkin, 2003; Caldart & Ricart 2003, Siggelkow & Levinthal, 2003; Siggelkow and Rivkin, 2003)

  • High level decisions impose “majority rule” to low level decisions

  • High level decisions made on the basis of bounded knowledge of the network’s payout (fitness) structure

  • Decomposability solved by bringing back “the designer” to the picture


Complexity and strategy eiasm academic council

An example”Corporate Level Decisions in Turbulent Environments:A View from Complexity Theory”Adrián Caldart & Joan Enric Ricart


Complexity and strategy eiasm academic council

Motivation

  • Long lasting (and open) debate on whether and how the

  • corporate level contributes to competitive advantage

  • CL contributes (Brush & Bromiley, 1997; Bowman & Helfat, 2001)

  • CL doesn’t contribute. (Schmalensee, 1985; Rumelt, 1991; Mc Gahan & Porter 1997)

  • Mixed results suggest that new approacheswould be welcomed

  • Recent literature focuses on design issues approached from the complexity paradigm

    • Case studies of companies exposed to “turbulent environments”

      • Turbulent environments: high dynamism, complexity and uncertainty

        (Galunic & Eisenhardt, 2001; Chakravarthy et al, 2001)

    • Agent based simulations exploring how design issues affect firm’s evolution

      (Levinthal, 1997; Mc Kelvey 1999; Gavetti and Levinthal, 2000)

      Research Question:

      • How does the corporate level affect competitive advantage in

      • turbulent environments?


Complexity and strategy eiasm academic council

Framework :Corporate Strategy Triangle

  • Purpose: to provide lenses to approach the field study

Cognition

Representing the fitness landscape

Imperfect due to bounded rationality

Corporate

Strategy

Architectural design

Management of interdependencies

Center-unit / Unit-Unit. Self-organization

Action-payoff relationships

Balance. Prevent “error” or “complexity”

catastrophes

Corporate search strategy

Local search

On line long jumps (commitment)

Off line long jumps (real options, alliances)

Recombination


Complexity and strategy eiasm academic council

Simulation experiments: Purpose

  • To explore the relationship between the three building blocks of the CST in a formal and general way

  • To observe the behavior and the relative performance of varied configurations of the CST under different environmental settings

  • Findings in a previous fieldwork (Caldart & Ricart; 2003) led us to explore a particular concern:

  • Environmental turbulence requires to increase internal

  • complexity (Ashby’s law). Then,

  • Should a change in internal complexity affect qualitatively

  • corporate strategy making?


Complexity and strategy eiasm academic council

Simulation experiments: Model

Adaptation of Kauffman’s NK model

Simulated firms have P=3 divisions with D=3 functional

policies each (N=9). Hierarchy of choices.

Parameter K is divided in two: KW (intra-divisional links)

and KB (interdivisional links)

Divisional strategy limited by majority rule

Eight possible corporate strategies (23)

Each CS has 64 possible configurations (43)

Firms are assumed to match environmental variety through

their architectural design (Ashby’s law)

Higher KW and KB imply an attempt to match internally a higher

degree of external turbulence

Software: Java-based ad-hoc program


Complexity and strategy eiasm academic council

Simulation experiments: Model

Seven Evolution Patterns (combinations of cognition and search strategy) are released on each kind of landscape:


Complexity and strategy eiasm academic council

Simulation experiments: Model

Each evolution pattern is released on eight kinds of fitness landscapes, each of them reflecting different structural designs

  • A Kb=0 implies an M-form design

  • As Kb increases, we have increasingly complex CM-form designs

  • 7 different patterns of evolution under eight different architectural designs conform 56 configurations of the CST


Complexity and strategy eiasm academic council

Simulation experiments: Simulation run II

Relatively Turbulent Environment


Complexity and strategy eiasm academic council

Simulation experiments: Findings


Complexity and strategy eiasm academic council

Simulation experiments: Findings

  • The importance of cognition is contingent to the degree of environmental turbulence

    • Stable environments: discipline ALWAYS pays

    • Turbulent environments: discipline only advisable if cognition is “intelligent”.

  • In turbulent environments, if the initial cognition is mediocre, results favor strategies based on its opportunistic application

    • Realized strategy as a mix of intended and emergent features

  • Purely opportunistic strategy always underperforms


Complexity and strategy eiasm academic council

General discussion and conclusions

Corporate Strategy

Decision level that drives, paces and frames corporate wide evolution through the choice, at the corporate level of the firm, of a particular equilibrium configuration of the CST.

Evolution is drivenby the cognitive representation

Corporate decisionspace evolutionshifting between initiatives that involve local search/long jumps/recombinations

The corporate level develops broad organizational arrangements that frame the emergence of self-organized processes as sources of corporate advantage


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